Performance of a Savonius vertical axis wind turbine installed on a forward facing step
Bibliographic record
Abstract
This study investigates the effects of installing a Savonius turbine on a forward-facing step to increase the power efficiency. Since the flow is disturbed by the step, the turbine may benefit from the accelerated flow found on top of the step. The flow field is solved using computational fluid dynamics in a 3D computational domain. The finite volume method is used to solve the Reynolds average Navier–Stokes governing equations and the SST k-ω turbulence equations. This simulation includes both rotating turbine and the step. The novelty of this work is to study this configuration with a power law wind velocity distribution as inlet velocity, which is more realistic in common applications, and to investigate a different turbine. The study shows that the Savonius turbine generates up to 73% more power when installed on a 5 m step. However, in case of a very high step, or a different turbine no improvement has been observed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".